
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "164"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 88 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 84 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.225913	-0.768982	-0.126639	-1.128845	2.525902	9.416521	-0.088601	-0.511398	0.8009	0.8041	0.2450	8.276358	9.119949
2459593	digital_ok	0.00%	0.00%	0.00%	0.00%	4.84%	0.54%	-0.560536	-0.942969	-0.792797	-0.485949	0.548915	0.575270	-0.333147	-1.013071	0.7423	0.7449	0.2475	2.142240	2.324068
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.356246	-0.591882	-0.467584	-0.576507	0.084181	1.726890	2.122273	0.004323	0.0373	0.0518	0.0191	nan	nan
2459591	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	0.00%	0.089727	-1.008391	-0.816578	-1.083887	1.364867	1.359945	2.108102	0.431101	0.6830	0.6971	0.3633	1.715610	1.698127
2459590	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	0.00%	0.625900	-1.029945	-0.539307	-1.146512	3.464798	0.708962	1.079706	0.958802	0.6838	0.6955	0.3558	1.875358	1.801717
2459589	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.302942	-1.076209	-0.188326	-1.213096	-0.460986	12.379437	-0.476270	-0.344826	0.7287	0.7252	0.2761	5.984886	5.896949
2459588	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	0.00%	0.308494	-1.131414	-1.126173	-0.946905	-0.524457	0.562205	-0.077224	-0.833687	0.7828	0.7663	0.2243	3.132514	2.531635
2459587	digital_ok	0.00%	-	-	-	2.80%	0.00%	0.329603	-1.213333	-0.740183	-0.779095	-0.492902	0.276831	0.855053	-0.187797	nan	nan	nan	0.989463	1.018201
2459586	digital_ok	0.00%	0.00%	0.00%	0.00%	7.03%	0.00%	0.479537	0.151344	0.164440	1.515630	1.041666	1.990630	1.568893	2.886377	0.6748	0.6883	0.3486	1.865508	1.792264
2459585	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.384835	3.462876	0.970985	3.434136	1.294772	3.645420	6.712244	2.415692	0.0469	0.0523	0.0165	0.000000	0.000000
2459584	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.60%	0.036194	-1.376373	-0.345772	2.923575	-1.217555	0.596936	0.106017	0.233060	0.6258	0.6354	0.3581	1.146993	1.129539
2459583	digital_ok	0.00%	0.00%	0.00%	0.00%	3.23%	0.00%	-0.203111	-0.650830	-0.678393	1.572572	-0.686151	2.035976	1.047882	2.120390	0.6869	0.6964	0.3721	1.887330	1.943984
2459582	digital_ok	0.00%	0.00%	0.00%	0.00%	2.70%	0.00%	0.034658	-0.555845	-0.714386	1.483821	-0.812892	2.162982	1.385442	2.041006	0.6878	0.6977	0.3767	1.830097	1.808135
2459581	digital_ok	0.00%	0.00%	0.00%	0.00%	3.24%	0.00%	-0.207830	-0.962885	-0.538771	1.062547	-0.003229	1.539115	0.773224	0.403180	0.6927	0.7031	0.3744	1.781790	1.741989
2459580	digital_ok	0.00%	0.00%	0.00%	0.00%	3.76%	0.00%	-0.350885	-0.681002	-0.882453	1.275768	-1.063677	2.245684	0.200408	0.199200	0.6893	0.6988	0.3740	1.913802	1.815986
2459579	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.251542	-0.544102	-0.703925	2.097248	-0.929890	1.913716	1.484170	1.045382	0.6810	0.6908	0.3804	0.000000	0.000000
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.319562	-0.211725	-0.813949	-0.644206	-0.556163	1.057190	1.123869	-0.748623	0.0363	0.0872	0.0141	nan	nan
2459577	digital_ok	0.00%	0.00%	0.00%	0.00%	10.27%	10.81%	-0.222410	-0.604531	-0.585404	1.156718	1.742405	1.976726	-0.061619	-0.007700	0.6867	0.6965	0.3778	1.834835	1.742567
2459576	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.646354	-0.088808	-0.737757	1.617646	0.780195	4.465472	-0.154622	1.871414	0.7129	0.7232	0.3738	7.483657	7.196620
2459575	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459574	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.702722	-0.133341	-0.878026	1.311587	-0.276215	12.325258	-0.840321	0.028307	0.7101	0.7192	0.3685	4.674080	4.653242
2459573	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	1.08%	-0.683347	-0.390444	-0.657183	2.070170	-0.275989	1.326237	0.609885	0.450635	0.7041	0.7151	0.3785	1.764501	1.657152
2459572	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.026162	-0.214396	-0.777135	1.504179	1.587685	12.936349	0.250454	2.784971	0.7096	0.7210	0.3748	6.156251	5.912837
2459571	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	0.011843	-0.189545	-0.724238	-0.699334	0.358266	5.054825	-0.151233	-0.402434	0.0376	0.0849	0.0207	nan	nan
2459570	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	1.62%	0.100162	1.093859	0.231090	1.097775	0.601351	1.859588	0.269204	0.906997	0.7004	0.7279	0.3580	1.558497	1.724899
2459569	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.00%	-1.176073	-0.648918	-0.856495	1.341143	-0.876843	3.526570	-0.832838	1.618877	0.7888	0.7940	0.2209	2.124944	2.060279
2459566	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.00%	-0.847649	-0.306457	-0.764182	1.547341	-0.619553	1.473792	-0.181532	-0.412308	0.7005	0.7148	0.3916	1.634767	1.659987
2459565	digital_ok	0.00%	-	-	-	100.00%	0.00%	0.042832	0.042832	0.026138	0.026138	-0.845420	-0.845420	-0.928844	-0.928844	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	0.00%	0.00%	0.00%	0.00%	5.20%	0.00%	-0.125884	-0.513730	-0.561825	-0.126649	-0.025850	2.399636	0.206404	0.508350	0.7177	0.7199	0.3886	1.840851	1.728887
2459563	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-0.492344	-1.199854	-0.788065	-0.468307	-0.947086	1.037697	1.231832	1.757402	0.7118	0.7190	0.3799	1.883995	1.820645
2459562	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.154713	0.071009	-0.156539	0.952365	0.394696	1.255923	0.602020	0.610098	0.7162	0.7247	0.3741	1.920751	1.748678
2459561	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.54%	-0.184386	0.154991	-0.223921	0.942068	0.607517	1.286321	0.967646	1.357386	0.7140	0.7243	0.3741	1.851825	1.759647
2459560	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.54%	0.002274	-0.142860	-0.030035	0.884161	0.643824	1.468986	0.683900	0.646634	0.7062	0.7163	0.3885	1.778355	1.687030
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-0.268041	0.033289	-0.193413	0.778849	0.342626	1.345664	0.561911	0.466583	0.7111	0.7185	0.3768	1.806191	1.687437
2459558	digital_ok	0.00%	0.00%	0.00%	0.00%	13.98%	0.00%	-0.762412	-0.476589	-0.818205	1.914059	-0.092280	1.709715	-0.347165	-0.000365	0.7150	0.7191	0.3862	1.710962	1.539942
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0352	0.0639	0.0173	nan	nan
2459556	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.287015	0.222773	-0.784779	3.342572	1.806216	23.056599	4.246561	84.493746	0.7086	0.7126	0.3844	4.609377	4.117915
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7183	0.7246	0.3898	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7166	0.7237	0.3911	nan	nan
2459552	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-1.212229	-0.412205	-0.557212	1.313873	-1.121353	0.572189	-0.749139	-0.572418	0.7209	0.7261	0.3918	1.295079	1.353430
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-0.154606	-0.043048	-0.275383	0.931909	0.536505	1.794978	0.174333	0.387537	0.7158	0.7250	0.3877	1.938924	1.695306
2459550	digital_ok	-	97.53%	97.53%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0428	0.0566	0.0092	nan	nan
2459549	digital_ok	0.00%	0.00%	0.00%	0.00%	8.11%	0.00%	-0.456036	-0.637990	-0.909932	1.533788	-0.528284	2.106374	0.198305	1.163739	0.7121	0.7190	0.3958	1.878678	1.661770
2459542	digital_ok	0.00%	0.00%	0.00%	0.00%	22.32%	0.00%	-0.470298	-1.031664	0.124119	0.359484	-0.739307	1.490589	0.587571	-0.336007	0.7670	0.7663	0.3437	1.906620	1.628316
2459541	digital_ok	100.00%	1.34%	1.79%	0.00%	100.00%	0.00%	-0.976629	-0.269540	-0.803707	3.208623	-0.649439	10.462542	-0.986454	5.804439	0.7610	0.7532	0.3193	5.290654	4.814055
2459540	digital_ok	0.00%	0.00%	0.00%	0.00%	21.88%	1.79%	-0.130829	-0.083511	-0.402506	0.892396	0.534914	-1.181739	0.079458	0.393329	0.7671	0.7641	0.3287	1.693971	1.588689
2459536	digital_ok	0.00%	0.00%	0.00%	0.00%	5.93%	0.00%	-0.008963	0.311023	-0.803733	1.795869	0.038531	1.465206	0.125589	0.203881	0.7434	0.7449	0.4096	2.097863	1.798196
2459535	digital_ok	0.00%	0.00%	0.00%	0.00%	21.43%	0.00%	0.070729	0.060167	-0.193430	0.676355	-0.543069	-0.043654	0.462188	0.175174	0.7993	0.7863	0.3455	2.014797	1.704077
2459534	digital_ok	100.00%	98.66%	98.66%	0.00%	100.00%	0.00%	2.904266	3.574159	-0.694714	0.219145	2.394829	4.322593	9.138831	9.103055	0.0600	0.0613	0.0100	1.282891	1.289130
2459533	digital_ok	0.00%	0.00%	0.00%	0.00%	4.89%	2.17%	0.054635	0.033927	-0.789205	0.947500	-0.508865	1.083910	1.083539	0.753025	0.7366	0.7379	0.3882	1.826203	1.707326
2459532	digital_ok	0.00%	0.00%	0.00%	0.00%	5.00%	2.78%	0.180261	0.349211	-0.767883	1.784732	-0.371989	0.604073	0.083967	-0.088353	0.7379	0.7420	0.3909	1.879711	1.727565
2459530	digital_ok	0.00%	0.00%	0.00%	0.00%	4.73%	0.00%	0.068970	0.375074	-0.269597	0.562229	-0.007288	1.076291	0.809798	0.251732	0.7565	0.7537	0.3797	1.937874	1.775927
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.766426	-0.221564	-0.945688	1.270927	0.217577	1.048848	0.026095	0.298094	0.9147	0.9400	0.2721	0.000000	0.000000
2459523	digital_ok	0.00%	-	-	-	12.24%	0.00%	0.041474	-0.009068	-1.095720	1.535257	-0.300074	0.230261	0.839134	0.508288	nan	nan	nan	2.422708	2.054309
2459522	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.159540	0.387271	-0.803753	1.607530	1.005817	0.414365	1.461588	0.411787	0.7219	0.7067	0.3080	1.936570	2.263316
2459513	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	-0.150591	-0.149213	-0.721790	-0.411239	1.567028	3.448797	8.508134	-0.449492	0.0334	0.0454	0.0066	nan	nan
2459508	digital_ok	0.00%	0.00%	0.00%	0.00%	81.58%	0.00%	-0.964485	0.327914	0.557480	1.202615	-0.095141	1.057409	0.336773	1.002544	0.9256	0.9519	0.1506	0.000000	0.000000
2459505	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.255942	-0.894411	-0.241390	1.212858	0.721387	1.882160	-0.040644	2.002374	0.0765	0.0731	0.0034	1.201607	1.205039
2459503	digital_ok	0.00%	0.00%	0.00%	0.00%	89.47%	0.00%	-0.112493	-0.223906	0.140309	0.848248	-0.316704	0.731364	-0.282814	1.035728	0.8811	0.8758	0.4169	0.000000	0.000000
2459501	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.296394	0.220596	-0.016113	0.697756	0.426686	-0.685191	-0.358379	0.462485	0.1179	0.1129	0.0118	0.000000	0.000000
2459500	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.042221	0.568032	0.503387	1.094961	-0.038525	0.984944	-0.076276	1.618100	0.1233	0.1153	0.0109	0.000000	0.000000
2459499	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.407011	0.970015	0.886875	1.302073	0.145859	0.718737	0.234360	1.950596	0.1195	0.1509	0.0075	0.000000	0.000000
2459498	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.330816	0.814716	0.041298	0.747984	-0.398301	0.911832	-0.399687	-0.011057	0.1172	0.1189	0.0127	0.000000	0.000000
2459497	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.316901	1.030690	-0.234389	0.540316	-1.140148	-0.031321	-0.548275	-0.010020	0.1436	0.1583	0.0158	0.000000	0.000000
2459496	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	0.00%	-0.353065	0.406819	0.326268	0.799915	-0.840909	-0.128573	-0.504877	0.115893	0.9262	0.9313	0.3368	0.000000	0.000000
2459495	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	0.00%	-0.235113	0.228621	0.297397	0.556864	-0.503843	-0.217733	-0.690432	-0.116241	0.9233	0.9273	0.3713	0.000000	0.000000
2459494	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	0.00%	0.284178	1.251598	0.704808	1.221526	-0.242873	1.343271	0.287778	3.570709	0.9065	0.9156	0.3867	0.000000	0.000000
2459492	digital_ok	0.00%	0.00%	0.00%	0.00%	50.00%	0.00%	-0.528101	-0.914685	-1.245430	-0.019384	0.029028	0.171475	0.216280	2.442074	0.7596	0.8113	0.3099	0.221195	0.159602
2459491	digital_ok	0.00%	0.00%	0.00%	0.00%	50.00%	0.00%	-0.580175	-1.027841	-1.352811	0.129160	0.001691	-0.099970	-0.735327	-0.127692	0.7990	0.8256	0.2460	0.868632	0.724533
2459490	digital_ok	0.00%	0.00%	0.00%	0.00%	47.37%	0.00%	-0.177302	-0.145758	-1.812609	-0.659476	-0.250135	0.943638	-0.168309	2.930842	0.7513	0.8078	0.3567	1.925814	1.734574
2459489	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.355511	-0.459723	-1.343949	1.670042	0.835879	53.710731	0.159467	9.245987	0.7707	0.8152	0.3073	6.010030	4.917365
2459488	digital_ok	0.00%	0.00%	0.00%	0.00%	85.71%	0.00%	-0.426020	-0.668110	-0.720496	0.184745	1.882364	0.579543	0.087814	2.476434	0.7721	0.8193	0.3368	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 164 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 164, Node 14:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 164: 2459612

Antenna 164: 2459611

Antenna 164: 2459610

Antenna 164: 2459609

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Antenna 164: 2459508

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Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶